System and method for estimating cardiorespiratory fitness

ABSTRACT

A method of determining a CRF level for a user of a fitness tracking system includes receiving activity data from at least one activity sensor carried by the user during a number of workouts, the activity data including distance data for each of the number of workouts, and then generating workout data based on the activity data. The method further includes storing the workout data in a memory, the memory further including demographic data for the user. When the an attribute of the workout data is less than a threshold number, the method includes determining a first CRF level for the user based on a first CRF model. When the attribute of the workout data is greater than the threshold number, the method includes determining a second CRF level for the user based on a second CRF model, and displaying the first and second CRF levels.

COPYRIGHT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

FIELD

The methods and systems disclosed in this document relate to the fieldof fitness tracking systems for monitoring user activity and, inparticular, to determining a cardiorespiratory fitness level associatedwith a user of a fitness tracking system.

BACKGROUND

Active individuals, such as walkers, runners, and other athletescommonly use fitness tracking systems to track exercise metrics such asaverage speed and distance traversed during an exercise session. Theseindividuals are typically interested in improving athletic performanceover time, including increased aerobic endurance, cardiovascular health,and/or overall fitness.

Cardiorespiratory fitness (CRF) is one tool often used to measureaerobic endurance, cardiovascular health and overall fitness. CRF refersto the ability of the circulatory and respiratory systems to supplyoxygen to skeletal muscles during sustained physical activity. A typicalstandard for measuring CRF is VO₂ max (also referred to herein as “VO2max”). VO2 max is the measurement of the maximum amount of oxygen thatan individual can utilize during intense, or maximal exercise (i.e.,“maximum oxygen uptake”). VO2 max is typically measured as millilitersof oxygen used in one minute per kilogram of body weight (ml/kg/min).

VO2 max is difficult to measure without specialized equipment. Accuratemeasurements of VO2 max are typically taken in a sports performance labusing ergometers, oxygen and carbon dioxide analyzers, heart ratemonitors, timers, and/or other equipment. In collecting data for atypical measurement, a high-intensity effort is performed on a treadmillor bicycle under a strict protocol. These protocols involve specificincreases in the speed and intensity of the exercise and collection andmeasurement of the volume and oxygen concentration of inhaled andexhaled air. Data collected is inserted into an equation and a score iscalculated.

Unfortunately, the need for specialized equipment prevents many athletesfrom knowing their CRF fitness score/level. Even if an athlete obtains aCRF fitness score at one time, there is no way for the athlete to knowif his or her CRF fitness score is improving without returning to theperformance lab and undergoing another CRF fitness analysis. In order toimprove the user experience of fitness tracking systems, it would bedesirable to provide users with an accurate estimation of current CRFfitness level without the need to repeatedly return to the performancelab. Accordingly, improvements in fitness tracking systems aredesirable.

SUMMARY

In at least one embodiment, a method of operating a fitness trackingsystem includes receiving first activity data from at least one activitysensor carried by a user during a first number of workouts performed bythe user within a period of time. First workout data is generated fromthe first activity data, and the first workout data is stored in amemory along with demographic data for the user. The method furthercomprises selecting a first model from a plurality of models fordetermining a cardiorespiratory fitness (CRF) level based at least inpart on the first number of workouts, determining a first CRF level forthe user based on the selected first model, and determining a firstconfidence rating for the first CRF level based at least in part on thefirst number of workouts. Thereafter, the first CRF level and the firstconfidence rating are displayed on a personal electronic deviceassociated with the user. Additionally, the method comprises receivingsecond activity data from the at least one activity sensor carried bythe user for a second number of workouts performed by the user over theperiod of time. Second workout data is generated from the secondactivity data, and stored in the memory. The method further comprisesselecting a second model from the plurality of models for calculating aCRF level based at least in part on the first number of workouts and thesecond number of workouts, determining a second CRF level for the userbased on the selected second model, the second model configured todetermining the second CRF level based at least in part on the firstworkout data and the second workout data, and determining a secondconfidence rating for the second CRF level based at least in part on thefirst number of workouts and the second number of workouts. Thereafter,the second CRF level and an associated second confidence rating aredisplayed on the personal electronic device associated with the user.

In another embodiment, a method of determining a CRF level for a user ofa fitness tracking system includes receiving activity data from at leastone activity sensor carried by the user during a number of workoutsperformed by the user within a period of time, and then generatingworkout data based on the activity data, the workout data including aplurality of workout attributes and associated values. The methodfurther includes storing the workout data in a memory, the memoryfurther including demographic data for the user. When the value of aworkout attribute is less than a threshold value, the method includesselecting a first CRF model, determining a first CRF level for the userusing the first CRF model, and displaying the first CRF level on apersonal electronic device associated with the user. When the value ofthe workout attribute is greater than the threshold value, the methodincludes selecting a second CRF model, determining a second CRF levelfor the user using the second CRF model, and displaying the second CRFlevel on the personal electronic device associated with the user.

In yet another embodiment, a method of determining a CRF level for auser of a fitness tracking system includes receiving activity data fromat least one activity sensor carried by the user during a number ofworkouts performed by the user within a period of time, and thengenerating workout data based on the activity data, the workout dataincluding distance data and speed/pace data for each of the number ofworkouts. The method further includes storing the workout data in amemory, the memory further including demographic data for the user,determining a CRF level for the user based on the demographic data, thedistance data, and the speed/pace data, and determining a confidencerating for the CRF level, the confidence rating based at least in parton the number of workouts performed by the user within the period oftime. The CRF level and the confidence rating are displayed on apersonal electronic device associated with the user, with the CRF levelbeing displayed as a maximum oxygen uptake score.

These and other aspects shall become apparent when considered in lightof the disclosure provided herein.

BRIEF DESCRIPTION OF THE FIGURES

The foregoing aspects and other features of a fitness tracking systemare explained in the following description, taken in connection with theaccompanying drawings.

FIG. 1 is a diagram illustrating a fitness tracking system, as disclosedherein;

FIG. 2 is a diagram illustrating a monitoring device of the fitnesstracking system of FIG. 1 ;

FIG. 3 is a diagram illustrating a personal electronic device of thefitness tracking system of FIG. 1 ;

FIG. 4 is a graph illustrating workout data for a plurality of workoutsperformed by a user of the fitness tracking system of FIG. 1 ;

FIG. 5 shows a personal electronic device and an associated display ofthe fitness tracking system of FIG. 1 ;

FIG. 6 is a block diagram illustrating method for determining a CRFlevel of a user using the fitness tracking system of FIG. 1 ;

FIG. 7 is a flowchart illustrating a first method of selecting a CRFmodel for the method of FIG. 6 ; and

FIG. 8 is a flowchart illustrating a second method of selecting a CRFmodel for the method of FIG. 6 .

All Figures © Under Armour, Inc. 2018. All rights reserved.

DETAILED DESCRIPTION

Disclosed embodiments include systems, apparatus, methods, and storagemedium associated for generating activity data corresponding to amovement of a user, and, in response thereto, selecting one of aplurality of CRF models based on the activity data, and determining oneor more CRF levels for the user based on the selected models.

For the purpose of promoting an understanding of the principles of thedisclosure, reference will now be made to the embodiments illustrated inthe drawings and described in the following written specification. It isunderstood that no limitation to the scope of the disclosure is therebyintended. It is further understood that this disclosure includes anyalterations and modifications to the illustrated embodiments andincludes further applications of the principles of the disclosure aswould normally occur to one skilled in the art to which this disclosurepertains.

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof wherein like numeralsdesignate like parts throughout, and in which is shown, by way ofillustration, embodiments that may be practiced. It is to be understoodthat other embodiments may be utilized, and structural or logicalchanges may be made without departing from the scope of the presentdisclosure. Therefore, the following detailed description is not to betaken in a limiting sense, and the scope of embodiments is defined bythe appended claims and their equivalents.

Aspects of the disclosure are disclosed in the accompanying description.Alternate embodiments of the present disclosure and their equivalentsmay be devised without parting from the spirit or scope of the presentdisclosure. It should be noted that any discussion herein regarding “oneembodiment,” “an embodiment,” “an exemplary embodiment,” and the likeindicate that the embodiment described may include a particular feature,structure, or characteristic, and that such particular feature,structure, or characteristic may not necessarily be included in everyembodiment. In addition, references to the foregoing do not necessarilycomprise a reference to the same embodiment. Finally, irrespective ofwhether it is explicitly described, one of ordinary skill in the artwould readily appreciate that each of the particular features,structures, or characteristics of the given embodiments may be utilizedin connection or combination with those of any other embodimentdiscussed herein.

Various operations may be described as multiple discrete actions oroperations in turn, in a manner that is most helpful in understandingthe claimed subject matter. However, the order of description should notbe construed as to imply that these operations are necessarily orderdependent. In particular, operations described may be performed in adifferent order than the described embodiments. Various additionaloperations may be performed and/or described operations may be omittedin additional embodiments.

For the purposes of the present disclosure, the phrase “A and/or B”means (A), (B), or (A and B). For the purposes of the presentdisclosure, the phrase “A, B, and/or C” means (A), (B), (C), (A and B),(A and C), (B and C), or (A, B and C).

The terms “comprising,” “including,” “having,” and the like, as usedwith respect to embodiments of the present disclosure, are synonymous.

Fitness Tracking System

As shown in FIG. 1 , a fitness tracking system 100 includes a monitoringdevice 104, a personal electronic device 108, and a remote processingserver 112. As disclosed herein, the fitness tracking system 100collects activity data 136 (see FIG. 2 ) with the monitoring device 104while the user performs a workout or otherwise exercises. At least oneof the monitoring device 104 and the personal electronic device 108generates workout data 238 (see FIG. 3 ) based on the activity data 136collected by the monitoring device 104. The fitness tracking system 100transmits and receives data over the Internet 124 using a cellularnetwork 128, for example. The fitness tracking system 100 may also beconfigured for use with a global positioning system (“GPS”) 132 and oneor more GPS devices. Each component of the fitness tracking system 100and method for operating the fitness tracking system 100 are describedherein.

The monitoring device 104 is configured to be worn or carried by a userof the fitness tracking system 100. The monitoring device 104 may beprovided in any number of different forms and configurations. In oneembodiment, the monitoring device 104 is permanently embedded in thesole of a shoe 150 worn by the user. The monitoring device 104 mayalternatively be configured for placement in the shoe 150, may beattached to the shoe 150, may be carried in a pocket 154 of the user'sclothing, may be attached to a hat 156 worn by the user, and/or may beattached to any portion of the user or the user's clothing oraccessories (e.g., wrist band, eyeglasses, necklace, visor, etc.).Moreover, in some embodiments, multiple monitoring devices may be usedto collect activity data, such as a monitoring device in a shoe and amonitoring device on a watch, or a left monitoring device 104 locatedand/or affixed to the user's left shoe 150 and a right monitoring device104 located and/or affixed to the user's right shoe 150.

In some embodiments, the monitoring device 104 includes a strap 158 tomount the monitoring device 104 onto the user. In this embodiment, themonitoring device 104 may be strapped to the user's wrist, arm, ankle,or chest, for example. In at least one embodiment, the strap 158 and themonitoring device 104 are provided as a watch or a watch-like electronicdevice. In a further embodiment, the monitoring device 104 is includedin a heartrate monitoring device (not shown) that is worn around thewrist, chest, or other body location that is typically used to measureheartrate. Thus, the monitoring device 104 is configured for mounting(permanently or removably) on any element of the user or the user'sclothing, footwear, or other article of apparel using any of variousmounting means such as adhesives, stitching, pockets, or any of variousother mounting means.

The monitoring device 104 is located proximate to and carried by theuser during workouts and other activities such as hiking, running,jogging, walking, and the like. The personal electronic device 108 mayalso be carried by the user during workouts and other activities.Alternatively, because the personal electronic device 108 is separatefrom the monitoring device, the personal electronic device 108 mayalternatively be left behind or remote from the user during suchworkouts and other activities. If the personal electronic device 108 iscarried by the user along with the monitoring device 104, data from themonitoring device 104 may be periodically sent to the personalelectronic device 108 during workouts and other activities. On the otherhand, if the personal electronic device 108 is not carried by the userduring a workout, data from the monitoring device 104 may be uploaded tothe personal electronic device 108 at the end of a workout when the twodevices are in sufficiently close proximity for communication.

Although the monitoring device 104 and the personal electronic device108 are described generally herein as completely separate devices, eachwith its own processor and housing, it will be recognized that in atleast some embodiments, the monitoring device 104 may be part of thepersonal electronic device 108. In such embodiments, the components ofthe monitoring device 104 are commonly housed with the personalelectronic device 108, and certain components may be shared, such as ashared processor.

With reference now to FIG. 2 , the monitoring device 104 (which may alsobe referred to herein as a “sensor device” or “measuring device”)includes a movement sensor 170 (which may also be referred to herein asan “activity sensor”), a transceiver 174, and a memory 178 each of whichis operably connected to a controller 182. The movement sensor 170 isconfigured to collect activity data 136, which corresponds to movementof the user during an exercise session. In one embodiment, the movementsensor 170 is an accelerometer sensor (such as a MEMS accelerometer) andthe activity data 136 is (or includes) acceleration data, whichcorresponds to acceleration of the user during the exercise session. Inthis embodiment, the movement sensor 170 collects acceleration data thatcorresponds to bipedal movement of the user. The activity data 136 isstored by the controller 182 in the memory 178. The movement sensor 170is provided as any type of sensor configured to generate the activitydata 136, such as a single-axis or a multi-axis microelectromechanical(MEMS) accelerometer, a gyroscope, and/or a magnetometer.

The transceiver 174 of the monitoring device 104, which is also referredto as a wireless transmitter and/or receiver, is configured to transmitand to receive data from the personal electronic device 108. In oneembodiment, the transceiver 174 is configured for operation according tothe Bluetooth® wireless data transmission standard. In otherembodiments, the transceiver 174 comprises any desired transceiverconfigured to wirelessly transmit and receive data using a protocolincluding, but not limited to, Near Field Communication (“NFC”), IEEE802.11, Global System for Mobiles (“GSM”), and Code Division MultipleAccess (“CDMA”).

The memory 178 of the monitoring device 104 is an electronic datastorage unit, which is also referred to herein as a non-transientcomputer readable medium. The memory 178 is configured to store theprogram instruction data 186 and the activity data 136 generated by themovement sensor 170. The program instruction data 186 includes computerexecutable instructions for operating the monitoring device 104. Theactivity data may be stored in the memory along with any otherelectronic data associated with the fitness tracking system 100, such asuser profile information, for example, or if the workout data isgenerated by the monitoring device 104, the workout data.

The controller 182 of the monitoring device 104 is configured to executethe program instruction data 186 for controlling the movement sensor170, the transceiver 174, and the memory 178. The controller 182 is aprovided as a microprocessor, a processor, or any other type ofelectronic control chip.

In at least one embodiment, the monitoring device 104 is equipped with aGPS receiver 176. The GPS receiver 176 of the monitoring device 104 isconfigured to receive GPS signals from satellites of the GPS 132 (seeFIG. 1 ). The GPS receiver 176 is further configured to generatelocation data that is representative of a current location on the Earthof the monitoring device 104 based on the received GPS signals. Thelocation data, in one embodiment, includes latitude and longitudeinformation. The controller 182 is configured to store the location datagenerated by the GPS receiver 176 in the memory 178 along with theactivity data.

With reference to FIG. 3 , the exemplary personal electronic device 108is configured as a smartphone. In other embodiments, the personalelectronic device 108 is provided as a smartwatch, an electronicwristband, a tablet computer, a desktop computer, or the like. In oneembodiment, the personal electronic device 108 is configured to be wornor carried by the user during collection of the activity data 136 by themonitoring device 104. In another embodiment, the personal electronicdevice 108 is not carried or worn by the user during collection of theactivity data 136, and the personal electronic device 108 receives theactivity data 136 from the monitoring device 104 after the usercompletes an exercise session. In a further embodiment, data may betransmitted from the monitoring device 104 to the personal electronicdevice 108 both during and after completion of an exercise session.

The personal electronic device 108 includes display 198, an input unit202, a transceiver 206, a GPS receiver 210, and a memory 214 each ofwhich is operably connected to a processor or a controller 218. Thedisplay 198 may comprise a liquid crystal display (LCD) panel configuredto display static and dynamic text, images, and other visuallycomprehensible data. For example, the display 198 is configurable todisplay one or more interactive interfaces or display screens to theuser including a display of at least an estimated distance traversed bythe user, a display of an estimated speed of the user, and a display ofan estimated stride length of the user. The display 198, in anotherembodiment, is any display as desired by those of ordinary skill in theart.

The input unit 202 of the personal electronic device 108 is configuredto receive data input via manipulation by a user. The input unit 202 maybe configured as a touchscreen applied to the display 198 that isconfigured to enable a user to input data via the touch of a fingerand/or a stylus. In another embodiment, the input unit 202 comprises anydevice configured to receive user inputs, as may be utilized by those ofordinary skill in the art, including e.g., one or more buttons,switches, keys, and/or the like.

With continued reference to FIG. 3 , the transceiver 206 of the personalelectronic device 108 is configured to wirelessly communicate with thetransceiver 174 of the monitoring device 104 and the remote processingserver 112. The transceiver 206 wirelessly communicates with the remoteprocessing server 112 either directly or indirectly via the cellularnetwork 128 (FIG. 1 ), a wireless local area network (“Wi-Fi”), apersonal area network, and/or any other wireless network over theInternet 124. Accordingly, the transceiver 206 is compatible with anydesired wireless communication standard or protocol including, but notlimited to, Near Field Communication (“NFC”), IEEE 802.11, Bluetooth®,Global System for Mobiles (“GSM”), and Code Division Multiple Access(“CDMA”). To this end, the transceiver 206 is configured to wirelesslytransmit and receive data from the remote processing server 112, and towirelessly transmit and receive data from the monitoring device 104.

The GPS receiver 210 of the personal electronic device 108 is configuredto receive GPS signals from satellites of the GPS 132 (see FIG. 1 ). TheGPS receiver 210 is further configured to generate location data 224that is representative of a current location on the Earth of thepersonal electronic device 108 based on the received GPS signals. Thelocation data 224, in one embodiment, includes latitude and longitudeinformation. The controller 218 is configured to store the location data224 generated by the GPS receiver 210 in the memory 214. The locationdata 224 may also be referred to herein as “GPS data.”

As shown in FIG. 3 , the memory 214 of the personal electronic device108 is an electronic data storage unit, which may also be referred toherein as a non-transient computer readable medium. The memory 214 isconfigured to store electronic data associated with operating thepersonal electronic device 108 and the monitoring device 104, includingprogram instruction data 228, activity data 136, workout data 238, CRFmodel data 240, and demographic data 242. The program instruction data228 includes computer executable instructions for controlling thepersonal electronic device. For example, the program instructions mayinclude computer executable instructions for generating the workout data238 based on the activity data 136 received from the monitoring device104.

The activity data 136 is the data received from the monitoring device104 or otherwise input by the user. The workout data 238 includes datarelated to each workout performed by the user when carrying themonitoring device 104. As noted previously, the workout data 238 isautomatically generated based on the activity data 136. The workout data238 includes a number of different workout attributes that define theworkout and associated values. For example, the workout attributes mayinclude type of workout, time, distance traversed, speed, pace, cadence,heart rate, stride length, ground contact time, ground contact timepercentage, foot strike pattern, efficiency, movement quality, fatigueindex, power output, or any of various additional workout attributes andvalues, including cumulative values, average values (such as mean,median or mode), instantaneous or split-time values within the workoutfor any of such attributes. As described in further detail below, in atleast one embodiment the workout attributes will at least includedistance data and speed and/or pace data for each workout, particularlywhen the workout is a walk or a run. (Because speed and pace are relatedcalculations that may be derived from one another, i.e., speed isdistance/unit time and pace is time/unit distance, speed data and pacedata may be referred to in the alternative herein as “speed/pacedata.”). Distance and speed/pace data for may be calculated based on theGPS data and/or the activity data using any of various known methods, aswill be recognized by those of ordinary skill in the art. While theworkout data has been described herein as being automatically generatedbased on the activity data, in at least one embodiment at least some ofthe workout data may be input manually by the user. For example, if auser conducted a workout and failed to wear the monitoring device 104,the user may be allowed to manually input workout data (e.g. timeduration, distance, pace, activity type, etc.).

In addition to data for a single workout, the workout data 238 mayfurther include cumulative workout data for a group of workoutsperformed over a period of time. For example, the workout data 238 mayinclude a total or an average attribute value for a group of run-typeworkouts performed within the past week or month. Exemplary cumulativeworkout data includes a total distance traversed over a period of time,an average distance per workout, an average speed/pace for the group ofworkouts, etc. The above-referenced attributes are exemplary, and thecontroller 218 and/or the controller 182 may be configured to performany of various calculations using the activity data 136 and/or theworkout data 238 in order to arrive at the cumulative workout data. Theworkout data 238 may be limited to workout data performed by the userwithin a given period of time (e.g., within the past year), or mayinclude all workout data ever generated for the user on the personalelectronic device 108.

The CRF model data 240 includes a number of different models fordetermining a CRF level of the user. As described in further detailbelow, each CRF model includes a set of rules that determines when themodel is used based on the available workout data 238 and demographicdata 242, and how the CRF level is determined based on such data. TheCRF levels determined from the CRF model data 240 may be provided indifferent forms such as a CRF score (e.g., between 20 and 80) or a CRFcategory (e.g., low, medium, high).

The demographic data 242 is based on demographic information of the userand may include one or more of various demographic identifiers for theuser such as gender, height, weight, body mass index (“BMI”), age, bodyfat percentage, resting heart rate, and other data. Any other userdemographic and/or physiological data may be included in the demographicdata 242. The demographic data 242 may also be referred to herein as“user profile data.”

The controller 218 of the personal electronic device 108 is operativelyconnected to the monitoring device 104 and is configured to execute theprogram instruction data 228 in order to control the components of thepersonal electronic device 108, including the display 198, the inputunit 202, the transceiver 206, the GPS receiver 210, and the memory 214.The controller 218 is provided as a microprocessor, a processor, or anyother type of electronic control chip. The controller 218 is configuredto process at least a subset of the activity data 136 and/or the GPSdata 224 and to calculate the workout data 238 by applying at least onerule of a set of rules to the subset of the activity data 136. Forexample, the controller 218 may be configured to calculate speed dataand distance data from a subset of the activity data 136 by integratingthe subset of the activity data 136. Alternatively, the controller 218may be configured to calculate speed data and distance data based on theGPS data 224.

With reference again to FIG. 1 , the remote processing server 112 isremotely located from the monitoring device 104 and the personalelectronic device 108. That is, the server 112 is located in a firstphysical location and the personal electric device 108 and themonitoring device 104 are located in a second physical location that isdifferent from the first physical location. The server 112 is configuredto receive and store the workout data 238 from the personal electricdevice 108 via the Internet 124. To this end, the server 112 includes atransceiver 252, a central processing unit (“CPU”) 264, and a memory256. Each of the transceiver 252 and the memory 256 is operablyconnected to the CPU 264.

The memory 256 of the remote processing server 112 includes programinstructions 260, the workout data 238, and CRF models 240. The server112 is configured to receive the activity data 136 and the workout data238 and store backup copies of the workout data 238, and/or generateadditional workout data, such as CRF levels for each user using the CRFmodels 240, when such CRF levels are not otherwise generated by thepersonal electronic device 108. Although not shown in FIG. 1 , it willbe recognized that the memory 256 may also include additional data, suchas the demographic data 242 for the user, or a copy of other datarelated to the user. Accordingly, it will be recognized that the remoteprocessing server 112 may be used as a backup storage location as wellas either a primary or secondary processing location for the workoutdata 238. In some embodiments of the fitness tracking system 100, all ofthe workout data and CRF levels are generated on the personal electronicdevice 108 without the user of the remote processing server. In otherembodiments, some or all of the workout data and CRF levels aregenerated on the remote processing server 112.

The transceiver 252 of the remote processing server 112 is configured towirelessly communicate with the personal electronic device 108 eitherdirectly or indirectly via the cellular network 128, a wireless localarea network (“Wi-Fi”), a personal area network, and/or any otherwireless network. Accordingly, the transceiver 252 is compatible withany desired wireless communication standard or protocol including, butnot limited to, Near Field Communication (“NFC”), IEEE 802.11,Bluetooth®, Global System for Mobiles (“GSM”), and Code DivisionMultiple Access (“CDMA”).

The CPU 264 of the remote processing server 112 is configured to executethe program instruction data 260 stored in the memory 256 for generatingand/or determining workout data 238, including CRF levels, by applyingone or more of the CRF models 240 to the workout data 238 and/or otherdata available to the remote processing server. The CPU 264 is providedas a microprocessor, a processor, or any other type of electroniccontrol chip.

Creation of CRF Models

The CRF models 240 stored on the remote processing server 112 and/or thepersonal electronic 108 includes a number of different regressionmodels. Each of these regression models may be used to estimate VO2 maxfor a user, and thus an associated estimated CRF level for the user.Each regression model includes a number of different inputs, which maybe based on demographic data, workout data, or other data. Examples ofinputs that may be used by different regression models include gender,age, body mass index (BMI), average heart rate, average pace, distancetraversed, a physical activity rating and/or a functional ability ratingbased on the workout data. Each regression model (of the CRF models 240)may be created by the owner/operator of the fitness tracking system 100or may be obtained from one or more third party sources. The regressionmodels may be linear (e.g., using linear regression) or non-linear(e.g., using polynomial regression or artificial neural network/machinelearning regression).

One example of a regression model that may be collected from a thirdparty is the publicly available regression model for estimating VO2 maxderived by Danielle Bradshaw (see Bradshaw, Danielle I., “An AccurateVO2 max Non-exercise Regression Model for 18 to 65 Year Old Adults”(2003). All Theses and Dissertations. Paper 1144, hereinafter, the“Bradshaw Paper”.) The Bradshaw Paper describes a regression equation topredict VO2 max based on non-exercise data. A laboratory assessment ofVO2 max was performed for the participants, and data was collected foreach participant including the participant's age, gender, body massindex (BMI), perceived functional ability (PFA) to walk, jog, or rungiven distances (based merely on the user's own subjective determinationof their ability to perform different workouts), and current physicalactivity (PA-R) level (based on recent physical activity reported by theparticipant). After charting the data and performing a regressionanalysis, the following regression model was derived:VO2max(mL·kg-¹·min-¹)=48.0730+(6.1779×gender)−(0.2463×age)−(0.6186×BMI)+(0.7115×PFA)+(0.6709×PA-R),

-   -   where for gender female=0 and male=1

(hereinafter, the “Bradshaw Model”).

While the Bradshaw Model is one possible regression model that may beused in arriving at a VO2 max calculation, it will be recognized thatnumerous regression models are contemplated herein as being included inthe CRF models 240. While the Bradshaw Model is based on the inputs ofgender, age, BMI, PFA and PA-R, different inputs are contemplated inother models. For example, a second regression model that is related tobut distinct from the Bradshaw Model uses only demographic inputs forthe user such as gender, age, weight, height, etc. As another example, athird regression model uses both demographic data and workout data, butdoes not include any perceived or actual functional ability level (e.g.,distance traversed during each workout may be used as an input, but noperformance data such as pace or heart rate is used as an input). As yetanother example, a fourth regression model uses both demographic dataand workout data, including both workout distance data and workoutperformance data (e.g., pace, heart rate, or other performance data maybe used as an input).

At least one exemplary regression model is a modified version of theBradshaw Model wherein the PA-R and PFA inputs are modified based onactual workout data. In this model, the regression model is as follows:VO2max(mL·kg-¹·min-¹)=48.0730+(6.1779×gender)−(0.2463×age)−(0.6186×BMI)+(0.7115×PFA′)+(0.6709×PA-R′),

-   -   where for gender female=0 and male=1,    -   PFA′ is a functional ability rating based on an athletic        performance metric derived from the workout data, and    -   PA-R′ is a physical activity rating that is based on an amount        of activity derived from the workout data.

The foregoing regression model may be referred to herein as the“Workout-Based Model.” As noted above, in the Workout-Based Model, boththe PA-R′ input and the PFA′ input are variable metrics that are derivedfrom the user's workout data. Because both PFA′ and PA-R′ are determinedbased on workout data, these inputs may also be referred to herein asworkout data or, alternatively, as workout features.

In the Workout-Based Model, PA-R′ is derived based on the total distancetraversed by the user during a period of time. In at least oneembodiment, this total distance is derived based on the workout data(e.g., the cumulative distance traveled over a number of workoutsperformed by the user over the period of time). In an alternativeembodiment, the total distance is based both on the workout data 238 andnon-workout data (e.g., other activity data generated by a step trackeror other activity tracking device). The period of time used to determinethe total distance traversed by the user may any number of differentperiods, such as days, weeks, months, etc. The following is an exemplarytable that illustrates a rule for assigning PA-R′ points to the userbased on distance travelled by the user:

Total Weekly Workout Distance PA-R′ Points Less than 0.1 miles 0 0.1 to0.25 miles 1 0.25 to 0.5 miles 2 0.5 to 0.75 miles 3 0.75 to 1 miles 4 1to 5 miles 5 5 to 10 miles 6 10 to 15 miles 7 15-20 miles 8 20-25 miles9 25-35 miles 10 35-50 miles 11 50-75 miles 12 More than 75 miles 13

While the foregoing rule for determining PA-R′ points is basedexclusively on distance travelled, it will be recognized that in otherembodiments other workout variables may be utilized to calculate thePA-R′ points, such as total workout time for the week.

In the Workout-Based Model, PFA′ is also based on the workout data 238,and particularly performance-type data. For example, PFA′ may be derivedbased on a set of rules that determine an estimated “typical workout”for the user from the workout data 238, and an associated “typicalworkout pace for the user.”

An example of workout data used to arrive at a typical workout for auser is shown by the graph 400 of FIG. 4 . In FIG. 4 , a numberdifferent workouts of a user are represented by the circles 410 of thegraph 400. Each workout 410 is defined at least in part by a distancetravelled during the workout (x-axis) and an average pace during theworkout (y-axis). Based on the information for the workouts 410, anumber of averages 420 can be calculated in order to define a typicalworkout for the user based on the total number of workouts. Theseaverages 420 that define the “typical workout” may be based on any ofvarious calculations derived from all of the workouts 4110, such as amean, median, mode, geometric median, etc. FIG. 4 shows a simple medianfor the workouts 410 (represented by the diamond-shaped marker on thegraph 400), a geometric median for the workouts (represented by thesquare-shaped marker on the graph 400), and a simple average/mean forthe workouts (represented by the asterisk marker on the graph 400). Dataassociated with one or more of these averages 420 is then used todetermine PFA′ points for the user. For example, the average pace of theuser during a “typical workout” may be used to arrive at the PFA′ pointsfor the user. The following is an exemplary table that illustrates arule for assigning a PFA′ points based on the average pace of the userduring a typical workout:

Average Workout Pace PFA′ Points Less than 2 mph 1 2 mph to 3 mph 2 3mph to 4 mph 3 4 mph to 5 mph 4 5 mph to 6 mph 5 6 mph to 7 mph 6 7 mphto 8 mph 7 8 mph to 9 mph 8 9 mph to 10 mph 9 10 mph to 11 mph 10 11 mphto 12 mph 11

While the foregoing rule for determining PFA′ points in theWorkout-Based Model is based exclusively on average pace, it will berecognized that in other embodiments other workout variables may beutilized to calculate the PA-R′ points, such as typical workoutdistance. In such embodiments, the PFA′ points may be increased ordecreased based on the typical workout distance. For example, if thetypical workout distance is less than 1.5 miles, the PFA′ points may bedecreased by 4, or if the typical workout distance is less than 2.5miles, the PFA′ points may be decreased by 2. As another example, if thetypical workout distance is greater than 7, the PFA′ points may beincreased by 2, and if the typical workout distance is greater than 10,PFA′ points may be increased by 4.

The foregoing description of the Workout-Based Model is but oneexemplary model that may be used by the fitness tracking system 100. Inat least one embodiment of the fitness tracking system 100, the CRFmodels 240 include alternative models that may be used based on theavailable data for the user, including the sufficiency and availabilityof both workout data 238 and demographic data 242. The sufficiency ofthe workout data 238 can be determined based on the quantity and type ofdata. For example, the number of workouts logged over a specified timeperiod must exceed a specified threshold number (e.g., ten workoutswithin a month), these workouts must contain at least workout distancedata (i.e., a total distance traversed for all the workouts), theworkout type must be for walking or running, and/or any of various otherrules for determining sufficiency of workout data 238. Similarly, thesufficiency of the demographic data 242 can be determined based on theexistence or non-existence of various data types. For example, thedemographic data must include at least age, gender, height, weight,and/or any of various other demographic data attributes. In such anembodiment, the CRF model that is selected may be based on a tieredsystem of sub-models in order to account for missing data or sparse data(i.e., missing or sparse workout data 238 or demographic data 242).

A first example of a tiered sub-model is a model that is implementedwhen demographic data 242 is sufficiently available (i.e., a full userprofile with all necessary inputs for the model is included in thedemographic data 242), but workout data 238 is missing or sparse (e.g.,the user has not logged any workouts or only a small number ofworkouts). According to this sub-model, only demographic data for theuser is used to arrive at an estimated CRF level.

A second example of a tiered sub-model is a model that is implementedwhen workout data 238 is sufficiently available (e.g., the user haslogged numerous workouts using the fitness tracking system 100 within apredetermined period of time), but demographic data is missing or sparse(e.g., one or more of age, gender, height, weight, BMI, etc. ismissing). According to this sub-model, only workout data for the user isused to arrive at the estimated CRF level. An example of such a modelwould be the Workout-Based Model discussed above but modified to onlyuse the PFA′ and PA-R′ inputs.

A third example of a group of tiered sub-models are those that areimplemented when workout data 238 and demographic data 242 are bothavailable, but some of this data is missing or sparse (e.g., an age ismissing from the demographic data 242 or the user has only logged asmall number of workouts). According to this sub-model, the workout data238 and the demographic data 242 for the user are both used to arrive atthe estimated CRF level, but the inputs are limited based on theavailability of the data. An example of such a model would be a modifiedversion of the Workout-Based Model discussed above wherein one or moreinputs are dropped out of the model, based on the availability of data.For example, if demographic data for age is not available, the age inputis removed from this model. As another example, if workout data islimited because pace data is not available (but total distance data isavailable), the PFA′ input may be dropped out of the model, but thePA-R′ input may remain in the model. According to these sub-models,inputs are dropped out based on certain types of data being unavailable(e.g., age or pace) within the workout data 238 and the demographic data242.

In addition to the foregoing it will be recognized that variousadditional embodiments where data is missing or sparse, the sufficiencyof available data may be based on one or more threshold values. Forexample, the model may be configured to only accept certain data whenthe user has completed a threshold number of workouts within a givenperiod of time. As an example, assume that according to one model, afirst threshold for a number of workouts within a month is seven and asecond threshold for a number of workouts within a month is demographicdata for a user is twelve. Also assume that a particular user hascomplete demographic data, and has completed ten workouts within amonth. In this instance, the workout data satisfies the firstsufficiency criteria (i.e., ten workouts within the month is greaterthan the threshold of seven), but the workout data fails the secondsufficiency criteria (i.e., ten workouts within the month is less thanthe threshold of fifteen). In this instance the model that is used toestimate a CRF level for the user incorporates the PA-R′ input, but doesnot incorporate the PFA′ input (i.e., it is determined that the numberof workouts is sufficient to provide a physical activity rating based ontotal distance of the user, but the number of workouts is insufficientto provide a true functional ability rating). This is but one example ofa sub-model that could be used based on limited or sparse workout data238. It will be recognized that various other input thresholds andcalculations are possible depending on the linear regression modelsutilized by the fitness tracking system.

A fourth example of a tiered sub-model is a model that is implementedwhen workout data 238 and demographic data 242 are both complete andfully available. According to this sub-model, both the workout data 238and the demographic data 242 for the user are used to arrive at theestimated CRF level. An example of such a model would be theWorkout-Based Model discussed above wherein demographic data 242 andworkout data 238 both satisfy some sufficiency criteria, and are fullyincorporated into the model.

In addition to the existence of various different CRF models, it will beappreciated that different CRF models may be selected at different timesfor use in calculating a CRF level for the user. Accordingly, a numberof different CRF models may be used within a single period of time inorder to arrive at different CRF levels for the user. Consider a userwho completes a first number of workouts within the first half of apredetermined time period (e.g., within the first fifteen days of amonth), and then completes a second number of workouts within the secondhalf of the predetermined time period (e.g., within the last fifteendays of the month). In this example, a first CRF model is selected whena CRF level is determined half-way through the month. Selection of thisfirst CRF model is based on (i) the first number of workouts beinggreater than a first threshold number, and (ii) the existence ofdistance data for the number of workouts. According to this model, thefirst CRF model is selected and the first CRF level is determined basedat least in part on both the demographic data of the user and thedistance data for the number of user workouts. Subsequently the userperforms the second number of workouts in the second half of the month,and a second CRF level for the user is determined within the month usinga second CRF model. Selection of the second CRF model is based at leastin part on (i) the first and second number of workouts being greaterthan a threshold number, (ii) an existence of distance data for thefirst and second number of workouts, and (iii) an existence ofspeed/pace data for each of the first and second number of workouts,wherein the second model determines the second CRF level based at leastin part on the demographic data, the distance data, and the speed/pacedata for the first and second number of workouts.

Calculation of CRF Levels

Each of the CRF models 240 are regression models that are configured tocalculate an estimated CRF level for the user. The CRF models areconfigured to calculate a CRF level for the user as either a numericalvalue (e.g., between 0 and 100) or a categorical rating (e.g., “low,”“medium,” “high”) and then transmit the estimated CRF level to theuser's personal electronic device 108 for display. According to at leastone embodiment, the determined CRF level for the user is simply a VO2max score (e.g., a score between 10 and 80 ml/kg/min) that is displayedfor the user on the personal electronic device 108. In otherembodiments, each model is configured to calculate a VO2 max score andthen translate that VO2 max score into a more descriptive CRF level,such as a CRF category or rating that is based in part on the user'sdemographic data such as age and gender. For example, after determininga VO2 max score, each CRF model may utilize a table to translate thedetermined score into a rating such as “excellent,” “good,” “aboveaverage,” “average,” “below average,” “poor,” or “very poor.” Again,this categorical rating for the user is typically based on variousdemographic factors for the user, such as gender and age. However, in atleast one embodiment, additional or different demographic factors may beconsidered when determining the user's CRF level. For example, based onthe demographic information collected for the user, the user may bepresented a CRF level that is based at least in part on geographicregion of residence, occupation, marital status, typical workout type(e.g., runner, biker, walker), etc. In at least one embodiment, the useris provided with the opportunity to define the demographic data used todetermine his or her CRF level relative to other users in of the fitnesstracking system. This feature provides the user with the ability tocompare himself or herself to other similarly situated individuals basedon factors other than just age and gender.

As noted previously, the CRF models 240 may be embedded in a processorof a cell phone or other personal electronic device, or may be embeddedin a remote server accessed via the internet. Accordingly, thecalculations for determining an estimate CRF level for each user may beperformed on the user's cell phone or other personal electronic device,or on a remote computer. In at least one embodiment, CRF levels arecalculated in a distributed manner using both a remote computer and apersonal electronic device of the user.

The determined CRF level may be presented to the user as a singlesummary value representing their current estimate of CRF level (e.g., ascalculated based on workout data received within the past week, month,year or years) or as a time series showing their cardiorespiratoryfitness estimate over time (e.g., a series of CRF levels as calculatedover a past number weeks, months or years). When the CRF level ispresented to the user as a time series, the user is able to quickly andeasily see his or her progress toward a fitness goal over time.

Confidence Ratings

In addition to presenting an estimated CRF levels to the user (whetheras a numerical value or as a categorical rating), the fitness trackingsystem 100 is also configured to provide the user with a confidencerating for the CRF level. This confidence rating may be a numericalvalue or score (e.g., a number between 1 and 10) or a categorical rating(e.g., “low,” “medium,” “high,” etc.). The confidence score provides theuser with an understanding of how accurate the estimated CRF level isfor the user. When a high confidence rating is presented, the user canbe confident that the associated CRF level is very close to the user'sactual CRF level as would be measured in a fitness lab. When a lowconfidence rating is presented, the user will understand that theassociated CRF level is likely to change over time as additional datafor the user (e.g., additional demographic data and/or workout data) iscollected by the fitness tracking system.

Confidence ratings for the estimated CRF levels calculated by thefitness tracking system 100 may be determined based on a number ofdifferent factors. In at least one embodiment, confidence ratings arebased on workout sample size available for the user in the workout data238. Accordingly, a higher confidence rating will be generated as thesample size of workouts within a given time window increases (e.g., auser with twelve workouts logged in a month will have a higherconfidence rating than a user with two workouts logged within a month).

In an alternative embodiment, confidence ratings are based on asensitivity analysis such as a leave-one-out sensitivity analysis.Accordingly, a higher confidence rating will be generated asleave-one-out variance decreases. For example, consider a situationwhere a time window for analysis includes workout data from tendifferent logged workouts. In this situation, the selected CRF model isrun ten separate times to produce ten separate VO2 max estimates basedon the ten unique subsets of data that each exclude the data from one ofthe workouts. The variance of these ten VO2 max estimates is calculated,and a higher confidence rating is associated with a lower variance (andvice-versa).

In yet another embodiment, confidence ratings are based simply on theselected CRF model such that a confidence rating is assigned based onlyon the CRF model used to arrive at the estimated CRF level. In thisembodiment, a CRF model that uses only demographic data but no workoutdata will have a low confidence rating. A CRF model that uses a limitedcombination of demographic data and workout data based on limitedavailability of such data will have a medium confidence rating. A CRFmodel that uses a combination of demographic data and workout data inwith full availability of such data will have a high confidence rating.

In at least one embodiment, the fitness tracking system provides anotification to the user in association with the displayed confidencerating, and particularly when the confidence rating is low. Thisnotification informs the user of the reason why the confidence rating islow. For example, the notification may inform the user to “update youruser profile to include your gender,” or “continue logging workouts.”

FIG. 5 shows an exemplary personal electronic device 108 running afitness tracking application as shown on a display screen 508 of thedevice 108. As shown in FIG. 5 , the display screen 508 shows adashboard for the user including a summary of workouts for the week. Thesummary includes total miles traversed 510, total workout time 512,total calories burned 514, and total number of workouts 516. Below theweekly summary is a CRF portion 520 of the display 508 that includes anumber of estimated CRF levels 522, and a confidence rating 524 for eachCRF level.

The CRF portion 520 of the display 508 includes a time series includesCRF levels 522 for the user in each of April, May and June of 2018. Theestimated CRF level 522 for April was 42 ml/kg/min, with a confidencerating 524 (referenced on the display as a “confidence score”) of “low.”The estimated CRF level 522 for the user in May was 47 ml/kg/min, with aconfidence rating 524 of “medium.” The estimated CRF level 522 for Junewas 45 ml/kg/min, with a confidence rating 524 of “high.” As discussedpreviously, the confidence ratings 524 for each of the CRF levels 522may have been determined using any of various means. For example, thelow confidence rating 524 for April could be the result of missing orsparse data (e.g., a small sample size as the result of a low number ofworkouts), a large variance resulting from a sensitivity analysis (evenif workout data was sufficient and complete for April), or a lowconfidence rating associated with the CRF model used to calculate theCRF level for the month, or some combination of such factors. The“medium” and “high” confidence ratings for May and June, respectively,result from similar reasons. In any event, based on the confidenceratings for April, May and June, the user can be relatively certain thathis or her VO2 max is near 45 because of the “high” confidence ratingfor June 2018.

In at least one embodiment, the fitness tracking system 100 providesadvice, recommendations, or offers to the user based on the estimatedCRF levels and the associated confidence scores. For example, if thefitness tracking system 100 detects that a particular user has a CRFlevel below the average CRF level for other users of the same age andgender, the fitness tracking system may provide recommendations forimproving the users CRF level. As another example, if the fitnesstracking system detects that a particular user has a high CRF level, thefitness tracking system may recommend that the user join a local club orother group of other high-level athletes.

In view of the foregoing, the user of the fitness tracking system 100 isprovided with a device that is capable of providing CRF levels for theuser over time, each of the CRF levels being associated with differentconfidence scores. This feature improves the fitness tracking system 100by allowing the fitness tracking system to provide the user withaccurate CRF levels without the need to visit a fitness laboratory tohave his or her CRF level measured using special equipment. Such highlyaccurate CRF levels have heretofore been impossible to determine withoutthe use of specialized equipment. The fitness tracking system 100 is asignificant improvement over prior systems that merely estimate a CRFlevel based on user-perceptions or user-reported data. The devices andmethods described herein improve on other fitness tracking systems byusing a numerous different CRF models, selecting one of the CRF modelsfor use based on the data available within the fitness tracking system,determining a CRF level and an associated confidence score, and thendisplaying the determined CRF level and confidence score to the user onhis or her personal electronic device.

Method for Estimating CRF Levels

With reference now to FIG. 6 , a block diagram 600 is shown illustratinga method of operating the above-described fitness tracking system 100 isillustrated. As illustrated in blocks 610 and 620, both workout data 238and demographic data 242 are used in the method. The demographic data242 is typically entered manually by the user of the fitness trackingsystem, but may be obtained in other ways, such as from otherapplications linked to the fitness tracking system, or from third partysources. The workout data 238 is generated from activity data receivedfrom at least one activity sensor provided on a monitoring device 104carried by the user during one or more workouts. The workout data 238may be generated at any of various locations such as the monitoringdevice 104, the user's personal electronic device 108, or a remoteserver 112 (as shown in FIG. 1 ). The workout data 238 and thedemographic data 242 are both stored in a memory and available for useaccording to the method. For example, as shown in FIG. 3 , both workoutdata 238 and demographic data 242 are stored in the memory 214 of theuser's personal electronic device 108.

As shown in block 630 of FIG. 6 , workout features for the user may bedetermined based on the workout data. These workout features may beutilized as inputs in association with various CRF regression models.For example, the workout features may include the PFA′ and PA-R′calculations used in the Workout-Based Model described previouslyherein. Because the workout features are derived from the workout data,these “workout features” are also considered to be part of the “workoutdata 238.”

As shown in block 640 of FIG. 6 , the method includes selecting at leastone CRF model from a plurality of models for determining a CRF level.This selection is based on one or more of the available workout data 238and demographic data 242. For example, in at least one embodimentdiscussed previously, the selection of a CRF model is based on a numberof workouts for the user over a period of time. If the number ofworkouts exceeds a threshold number, one CRF model may be selected. Ifthe number of workouts is less than the threshold number a different CRFmodel is selected. An example of this selection process is described infurther detail below with reference to FIG. 7 . In another example, theselection of CRF models is based on the availability of distance dataand/or speed/pace data. An example of this selection process isdescribed in further detail below with reference to FIG. 8 .

With continued reference to FIG. 6 , block 650 shows that after a CRFmodel (i.e., a “regression model”) is selected, various inputs areinserted into the regression model. These inputs include one or more ofthe demographic data 242 (as shown in block 620), the workout data 238(as shown in block 610), and the associated workout features (as shownin block 630).

As shown in block 660, after the appropriate inputs are inserted intothe selected CRF model (i.e., the selected “regression model”), a VO2max estimation is output from the model. Based on this VO2 maxestimation, a CRF level is determined along with a confidence rating forthe CRF level. As discussed previously, the confidence rating may bebased on numerous factors. For example, the confidence rating may bebased on a number of workouts for the user within the predeterminedperiod of time. Both the determined CRF level and the confidence ratingare then displayed for the first user on the personal electronic device108.

After determination and display of a first CRF level, as describedabove, the method of FIG. 6 is repeated at a later time to provide asecond CRF level. In particular, the method is periodically repeated inorder to determine multiple updated CRF levels for the user. Each timethe method is repeated, the activity tracking system selects anappropriate CRF model for use based on the available workout data 238and demographic data 242. Each of these subsequent CRF levels isassociated with a confidence rating. It will be recognized that theconfidence rating for each subsequent CRF level will likely increaseover time as additional workout data and/or demographic data iscollected for the user and available to the fitness tracking system indetermining the CRF level of the user. However, it is possible for theconfidence rating for a subsequent CRF level to decrease over time. Forexample, confidence ratings for CRF levels may decrease when the userworkouts less often, or when workout data or demographic data is lost orremoved for some reason.

With reference now to FIG. 7 , a flowchart 700 is shown for an exemplarymethod for selecting a CRF model from the plurality of stored CRF models240 (which process was referenced in block 640 of FIG. 6 ). The method700 begins with step 710 where it is determined whether the sufficientdemographic data for the user is available (e.g., a determination thatage, gender, height and weight all exist for the user in thedatabase/memory 214). If sufficient demographic data is not availablefor the user, the method moves to step 720.

At step 720, a determination is made whether the number of workouts forthe user within a predetermined period of time (e.g., a month) isgreater than a first threshold (e.g., zero, one, two, three workouts,etc.). If the number of workouts is not greater than the first thresholdnumber, the method moves to step 760, and a determination is made thatno CRF model is available, and no CRF level will be presented to theuser on the display 508. Instead, a message may be presented to the useron the display 508 that an estimate of the user's CRF level will beprovided after the user completes the demographic information in a userprofile or performs additional workouts.

Returning to step 720, if the number of workouts is greater than thefirst threshold, the method moves to step 740. At step 740, adetermination is made whether the total number of workouts for the userwithin the period of time is greater than a second threshold (e.g.,five, ten, fifteen workouts, etc.). If the number of user workouts isless than the second threshold, the method moves to step 761, and themethod selects a first model from the plurality of CRF models 240 foruse in determining a CRF level for the user. In this case, the firstmodel is a regression model that is uniquely configured to estimate aCRF level based only on a limited amount of workout data and withoutdemographic data. The confidence rating associated with this model willbe relatively low, and therefore a message may be displayed to the userexplaining that the confidence rating of the estimated CRF level may beincreased by taking further action, such as entering additionaldemographic data in the user profile and/or performing additionalworkouts. If the number of workouts is greater than the secondthreshold, the method moves to step 762, and the method selects a secondmodel from the plurality of CRF models 240 for use in determining a CRFlevel for the user. In this case, the second model is a regression modelthat is uniquely configured to estimate a CRF level based only on a morecomplete set of workout data but still without demographic data.

Returning to step 710, if sufficient demographic data is available forthe user, the method moves to step 730. At step 730, a determination ismade whether the number of workouts for the user over a predeterminedperiod of time (e.g., a month) is greater than a third threshold (e.g.,zero, one, two, three workouts, etc.). If the number of workouts is notgreater than the third threshold number, the method moves to step 763,and a third CRF model is selected for use in determining a CRF level ofthe user. This CRF model may be, for example, based exclusively on thedemographic data for the user. On the other hand, if the number ofworkouts is greater than the third threshold in step 730, the methodmoves to step 750. At step 750, a determination is made whether thetotal number of workouts for the user within the period of time isgreater than a fourth threshold (e.g., five, ten, fifteen workouts,etc.). If the number of user workouts is less than the fourth threshold,the method moves to step 764, and the method selects a fourth model fromthe plurality of CRF models 240 for use in determining a CRF level forthe user. In this case, the fourth model is a regression model that isuniquely configured to estimate a CRF level based on completedemographic data but only a limited amount of workout data. If thenumber of workouts is greater than the fourth threshold in step 750, themethod moves to step 765, and the method selects a fifth model from theplurality of CRF models 240 for use in determining a CRF level for theuser. In this case, the fifth model is a regression model that isuniquely configured to estimate a CRF level based on a complete set ofworkout data and a complete set of demographic data.

It will be recognized that after selection of the appropriate CRF model,the user's CRF level is calculated based on the selected regressionmodel. The calculated CRF level is displayed to the user on the display508 of the user's personal electronic device 108. A confidence rating isalso displayed to the user in association with the CRF level. Thisconfidence rating may be based at least in part on the selected model.Accordingly, for each of CRF models 761-765, the associated confidencerating may be different.

With reference now to FIG. 8 , a flowchart 800 is shown for anotherexemplary method for selecting a CRF model from the plurality of storedCRF models 240 (which process was referenced in block 640 of FIG. 6 ).It will be recognized that the method 800 of FIG. 8 is similar to themethod 700 of FIG. 7 , but different workout data is analyzed todetermine which CRF model to use in determining a CRF level for theuser. The method 800 begins with step 810 where it is determined whetherthe sufficient demographic data for the user is available (e.g., adetermination that age, gender, height and weight all exist for the userin the database/memory 214). If sufficient demographic data is notavailable for the user, the method moves to step 820.

At step 820, a determination is made whether the distance traversed bythe user over a predetermined period of time (e.g., a month) is greaterthan a first threshold (e.g., zero, one, five, ten miles, etc.). If thedistance traversed by the user is not greater than the first thresholdnumber, the method moves to step 860, and a determination is made thatno CRF model is available, and no CRF level will be presented to theuser on the display 508. Instead, a message may be presented to the useron the display 508 that an estimate of the user's CRF level will beprovided after the user completes the demographic information in a userprofile or performs additional workouts.

Returning to step 820, if the distance traversed by the user over thepredetermined period of time is greater than the first threshold, themethod moves to step 840. At step 840, a determination is made whetherthe total distance traversed by the user within the period of time isgreater than a second threshold (e.g., five, twenty, fifty, one-hundredworkouts, etc.). If the number of user workouts is less than the secondthreshold, the method moves to step 761, and the method selects a sixthmodel from the plurality of CRF models 240 for use in determining a CRFlevel for the user. In this case, the sixth model is a regression modelthat is uniquely configured to estimate a CRF level based only on alimited amount of workout data and without demographic data. If thenumber of workouts is greater than the second threshold at step 840, themethod moves to step 862, and the method selects a seventh model fromthe plurality of CRF models 240 for use in determining a CRF level forthe user. In this case, the seventh model is a regression model that isuniquely configured to estimate a CRF level based only on a morecomplete set of workout data but still without demographic data.

Returning to step 810, if sufficient demographic data is available forthe user, the method moves to step 830. At step 830, a determination ismade whether the distance traversed by the user during workouts over apredetermined period of time (e.g., a month) is greater than a thirdthreshold (e.g., zero, one, five, ten miles, etc.). If the number ofworkouts is not greater than the third threshold number, the methodmoves to step 863, and an eighth CRF model is selected for use indetermining a CRF level of the user. This CRF model may be, for example,based exclusively on the demographic data for the user. On the otherhand, if the distance traversed by the user is greater than the thirdthreshold in step 830, the method moves to step 850. At step 850, adetermination is made whether the distance traversed by the user duringworkouts within the period of time is greater than a fourth threshold(e.g., ten, twenty, fifty miles, etc.). If the distance traversed by theuser is less than the fourth threshold, the method moves to step 864,and the method selects a ninth model from the plurality of CRF models240 for use in determining a CRF level for the user. In this case, theninth model is a regression model that is uniquely configured toestimate a CRF level based on complete demographic data but only alimited amount of workout data. If the distance traversed by the user isgreater than the fourth threshold in step 850, the method moves to step865, and the method selects a tenth model from the plurality of CRFmodels 240 for use in determining a CRF level for the user. In thiscase, the tenth model is a regression model that is uniquely configuredto estimate a CRF level based on a complete set of workout data and acomplete set of demographic data.

It will be recognized that after selection of the appropriate CRF model,the user's CRF level is calculated based on the selected regressionmodel. The calculated CRF level is displayed to the user on the display508 of the user's personal electronic device 108. A confidence rating isalso displayed to the user in association with the CRF level. Thisconfidence rating may be based at least in part on the selected model.Accordingly, for each of CRF models 761-765, the associated confidencerating may progressively increase with the first through the fifthmodels.

In at least one embodiment an ensemble model approach may be utilized toarrive at an estimation of a CRF level for the users. In this approach,each of a plurality of different CRF models are applied to the availabledata for the user. This results in a number of different estimates ofCRF levels for the user (i.e., a CRF level for each model used). Aftercalculating the plurality of CRF levels, an average value for theplurality of CRF levels, such as a mean or median value, is used as theestimate of CRF level for the user. This method also has the benefit ofproviding uncertainty associated with the prediction based on thevariance in the prediction model results. The ensemble method may alsobe implemented in a recursive manner in order to fine-tune the resultsfor the user over time, and thereby provide an even more accurateestimation of the user's CRF level.

The fitness tracking system 100 described herein results in animprovement over past fitness tracking systems by providing multiple CRFlevels to the user over time, each of which is based on a CRF model thatis automatically selected by the system and then applied to the user.Different CRF models are available in the system, wherein each of thedifferent CRF models are utilized depending upon the available userdata. Furthermore, each of the CRF levels is associated with aconfidence score and may include instructions to the user on how toimprove the confidence score of the estimated CRF level. The detailedselection process for determining a CRF model to be used in arriving ata CRF level estimate provides significant advances over past systemsthat merely estimated one CRF level based on manual user inputs and/orvery limited sets of data. The fitness tracking system described hereinprovides not only accurate CRF levels derived outside of a laboratoryenvironment, but CRF levels that dynamically adjust as the usercontinues to use the fitness tracking system. With improved CRF levelsand associated confidence scores, users of the fitness tracking systemare provided with novel performance metrics and the ability to tailortraining routines based on their known CRF level.

While the disclosure has been illustrated and described in detail in thedrawings and foregoing description, the same should be considered asillustrative and not restrictive in character. It is understood thatonly the preferred embodiments have been presented and that all changes,modifications and further applications that come within the spirit ofthe disclosure are desired to be protected.

It will be appreciated that the foregoing aspects of the presentdisclosure, or any parts or functions thereof, may be implemented usinghardware, software, firmware, tangible non-transitory computer readableor computer usable storage media having instructions stored thereon, ora combination thereof, and may be implemented in one or more computersystems.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the disclosed embodiments ofthe disclosed device and associated methods without departing from thespirit or scope of the disclosure. Thus, it is intended that the presentdisclosure covers the modifications and variations of the embodimentsdisclosed above provided that the modifications and variations comewithin the scope of any claims and their equivalents.

What is claimed is:
 1. A method of operating a fitness tracking system,the method comprising: receiving first activity data from at least oneactivity sensor carried by a user during a first number of workoutsperformed by the user within a period of time; generating first workoutdata from the first activity data; storing the first workout data in amemory, the memory further including demographic data for the user;selecting a first model from a plurality of models for determining acardiorespiratory fitness (CRF) level based at least in part on thefirst number of workouts; determining a first CRF level for the userbased on the selected first model; determining a first confidence ratingfor the first CRF level based at least in part on the first number ofworkouts; displaying the first CRF level and the first confidence ratingon a personal electronic device associated with the user; receivingsecond activity data from the at least one activity sensor carried bythe user for a second number of workouts performed by the user over theperiod of time; generating second workout data from the second activitydata; storing the second workout data in the memory; selecting a secondmodel from the plurality of models for calculating a CRF level based atleast in part on the first number of workouts and the second number ofworkouts; determining a second CRF level for the user based on theselected second model, the second model configured to determining thesecond CRF level based at least in part on the first workout data andthe second workout data; determining a second confidence rating for thesecond CRF level based at least in part on the first number of workoutsand the second number of workouts; and displaying the second CRF leveland an associated second confidence rating on the personal electronicdevice associated with the user.
 2. The method of claim 1 whereinselecting the first model is based on the first number of workouts beingless than a threshold number, wherein the first model is configured todetermine the first CRF level based exclusively on the demographic datafor the user in the memory.
 3. The method of claim 1 wherein selectingthe first model is based on the first number of workouts being greaterthan a threshold number, wherein the first model is configured todetermine the first CRF level based exclusively on the first workoutdata for the user in the memory.
 4. The method of claim 1 whereinselecting the second model is based a total of the first number ofworkouts and the second number of workouts being greater than athreshold number, wherein the second model is configured to determinethe second CRF level based on the demographic data, the first workoutdata, and the second workout data for the user in the memory.
 5. Themethod of claim 1 wherein the first workout data and the second workoutdata includes at least one of distance data and speed/pace data for eachof the first number of workouts and the second number of workouts. 6.The method of claim 5 wherein selecting the first model is based atleast in part on (i) the first number of workouts being greater than athreshold number, and (ii) an existence of distance data for the firstnumber of workouts, wherein the first model determines the first CRFlevel based at least in part on the demographic data and the distancedata for the first number of workouts.
 7. The method of claim 6 whereinselecting the second model is based at least in part on (i) the firstand second number of workouts being greater than a threshold number,(ii) an existence of distance data for the first and second number ofworkouts, and (iii) an existence of speed/pace data for each of thefirst and second number of workouts, wherein the second model determinesthe second CRF level based at least in part on the demographic data, thedistance data, and the speed/pace data for the first and second numberof workouts.
 8. The method of claim 7 wherein the second modeldetermines the second CRF level based at least in part on an average ofthe speed/pace data for the first and second number of workouts.
 9. Themethod of claim 8 wherein the average is a mean, a median or a mode. 10.The method of claim 1 wherein each of the first number of workouts andthe second number of workouts are walk workouts or run workouts.
 11. Themethod of claim 1 wherein the first CRF level and the second CRF levelare simultaneously displayed on the personal electronic devices as atime series of CRF levels.
 12. The method of claim 1 wherein each of theplurality of models is associated with at least one confidence ratingsuch that the first confidence rating is based at least in part on theselection of the first model and the second confidence rating is basedat least in part on the selection of the second model.
 13. The method ofclaim 12 wherein at least one of the plurality of models is associatedwith a range of confidence ratings, and wherein each confidence ratingin the range of confidence ratings is associated with a number ofworkouts.
 14. The method of claim 1 wherein the second confidence ratingis based on a sensitivity analysis for a CRF level for the first andsecond number of workouts performed within the period of time, whereinthe sensitivity analysis includes determining a variance and theconfidence rating is based at least in part on the variance.
 15. Themethod of claim 14 wherein the sensitivity analysis is a leave-one-outsensitivity analysis that calculates a plurality of CRF levels, whereinat least one of the first and second number of workouts is excluded fromeach of the calculated plurality of CRF levels, and wherein the varianceis based on each of the calculated plurality of CRF levels.
 16. A methodof determining a cardiorespiratory fitness (CRF) level for a user of afitness tracking system, the method comprising: receiving activity datafrom at least one activity sensor carried by the user during a number ofworkouts performed by the user within a period of time; generatingworkout data based on the activity data, the workout data including aplurality of workout attributes and associated values; storing theworkout data in a memory, the memory further including demographic datafor the user; when the value of a workout attribute is less than athreshold value, selecting a first CRF model, determining a first CRFlevel for the user using the first CRF model, and displaying the firstCRF level on a personal electronic device associated with the user,wherein said workout attribute is one of time, distance traversed,speed, pace, cadence, heart rate, stride length, ground contact time,ground contact time percentage, foot strike pattern, efficiency,movement quality, fatigue index, and power output; and when the value ofthe workout attribute is greater than the threshold value, selecting asecond CRF model, determining a second CRF level for the user using thesecond CRF model, and displaying the second CRF level on the personalelectronic device associated with the user, wherein the second CRF modelis different from the first CRF model.
 17. The method of claim 16wherein the workout attribute is total workout distance within a periodof time, wherein the determined first CRF level is based exclusively onthe demographic data, and wherein the determined second CRF level isbased at least in part on the workout data.
 18. The method of claim 17wherein workout data further comprises speed/pace data for each of theplurality of workouts, and wherein the determined second CRF level isbased on the demographic data, the distance data, and the speed/pacedata for each of a plurality of workouts.
 19. A method of determining acardiorespiratory fitness (CRF) level for a user of a fitness trackingsystem, the method comprising: receiving activity data from at least oneactivity sensor carried by the user during a number of workoutsperformed by the user within a period of time; generating workout databased on the activity data, the workout data including distance data andspeed/pace data for each of the number of workouts; storing the workoutdata in a memory, the memory further including demographic data for theuser; determining a CRF level for the user based on the demographicdata, the distance data, and the speed/pace data; determining aconfidence rating for the CRF level, the confidence rating based atleast in part on the number of workouts performed by the user within theperiod of time; and displaying the CRF level and the confidence ratingon a personal electronic device associated with the user, wherein theCRF level is displayed as a maximum oxygen uptake score.
 20. The methodof claim 19 wherein the confidence rating is based on a determination ofmultiple CRF levels for the number of workouts and a sensitivityanalysis for the multiple CRF levels, wherein the sensitivity analysisincludes determining a variance, and wherein the confidence rating isbased at least in part on the variance.